the impact of governance on economic growth: the case of middle

Topics in Middle Eastern and African Economies Vol. 18, Issue No. 1, May 2016

126

The Impact of Governance on Economic Growth:

The Case of Middle Eastern and North African

Countries

Noha Emara, Ph.D.1

And I-Ming Chiu, Ph.D.

JEL Classification: O16; O43; N20

Keywords: MENA; Governance; Composite Governance Index;

Economic Growth

1 Rutgers University, the State University of New Jersey, Camden


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Abstract

The main goal of this paper is to evaluate the impact of governance on economic growth

using a group of 188 countries. Although our main focus is on the 21 Middle Eastern and

North African (MENA) countries, our findings can be applied to the other countries as

well. We create a “composite governance index” (CGI) that summarizes the existing six

governance measurements in the Worldwide Governance Indicators (WGI), using the

Principal Components Analysis (PCA) method. The first principal component derived

from the WGIs explains as much as 81% of the variations in the original six WGI

measurements. We then use PPP adjusted constant per capita GDP data to find that per

capita GDP would rise by about 2% if the CGI increases by one unit. Using the Rule of

70, the marginal estimate further indicates a mere five-unit improvement in CGI would

double the country’s per capita GDP in seven years. Nonetheless, the effect of

improvement of governance cannot account for the higher than expected per capita GDP

in most of the oil rich MENA countries. In other words, the majority of the MENA

countries have achieved fragile levels of economic growth that does not depend on sound

governance.

I. Introduction

There is no doubt that improving the business climate is a major factor for attracting both

national and international investors to a country, which would ultimately be reflected in

increasing economic growth. Investors will drive away from a politically unstable,

bureaucratic, and highly corrupted economies with inefficient and nontransparent

government services. A government that is socially accountable in delivering services

and responsive to the needs of its citizens will ultimately create a democratic

environment leading to inclusive growth and human development.

The slow growth performances in many developing countries, especially Middle

East and North African (MENA) countries, have been disappointing over the last decade.

Since the second half of the 1980’s, growth and development studies have started to shed

the light on the importance of improving institutions of governance on economic growth.

The studies of Owens (1987) and Sen (1999) show that economic and political stability

has a statistical significant impact on economic growth and development.

Many scholars and researchers have confirmed the positive link of improved

quality of governance on economic growth. The study of Knack and Keefer (1997) shows

that both property rights and contract enforcement have positive impact on economic

growth. Similarly, Campos and Nugent (1999) prove a statistically significant positive

impact of governance on economic development. The work of Kaufmann, et al. (1999a

and 1999b) reaches the same conclusion about the importance of governance to economic


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development. Similar findings are reached in the work of Knack and Keefer (1995) and

Mauro (1995).

Much research work conducted by the International Monetary Fund (IMF), the

United Nations, and the World Bank shows that good governance leads to economic

growth. For instance, Kaufman and Kraay (2002) evaluated the World Governance

Indicators over the period 1996 to 2002 and found a positive relationship between per

capita income and quality of governance.

One of the leading studies in the literature on institutions and their effect on

economic performance was written by Acemoglu, et al. (2000). The paper shows that

differences in economic performance among nations can be attributed to the difference in

institutions. Acemoglu, et al. found that different colonization strategies have led to

different types of institutions that remain today. Colonies with low mortality rates had

higher European settlements and accordingly stronger institutions were built which

ultimately explains differences between countries in terms of current performance.

Furthermore, the work of Acemoglu, et al. (2005b) concludes that differences between

countries in terms of income and economic development are explained by differences in

institutions. Within the same lines, Acemoglu and Robinson (2008) show that differences

in economic prosperity among nations can be explained by differences in political

institutions. Their paper provides policy recommendations that suggest reforming

institutions would help in poverty alleviation. Additionally, the work of Chauvet and

Collier (2004) finds that developing countries with poor quality of governance will lead

to less economic growth. And within the same lines, the cross sectional of study by

Acemoglu and Robinson (2012) compares adjacent cities along the United States-Mexico

border. They reach the conclusion that political and economic institutions underlie

economic success and the degree of incentive structures and the state-market relationship

is the determinant factor of cities’ growth performance.

Given the previous background, the research on the link between governance and

economic growth for the MENA region is relatively very thin. The World Bank’s World

Governance Indicator project shows that the MENA region always ranks below the

average of the sample. This World Bank project seeks to measure the quality of

governance in a particular nation using six metrics: voice and accountability, political

stability, government effectiveness, regulatory quality, rule of law, and control of

corruption. These metrics are measured both by a governance score that ranges from -2.5

to +2.5, and a percentile rank relative to nations worldwide.

The study of Leenders and Sfakianakis (2002) shows that the Transparency

International’s Corruption Perceptions Index for Egypt, Morocco, Jordan, Tunisia and

Libya is below the global median in terms of levels of public sector corruption. Similarly,

the World Bank (2003) study shows that the MENA countries perform lower than

countries with similar incomes and characteristics. In addition, Chêne (2008) shows that


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based on the World Bank Governance Indicators, MENA countries perform above

average in political stability, rule of law, and quality of administration, however, it

performs below average for the transparency, voice accountability, and control of

corruption.

Within the same lines, Han, X., Khan, H., and Zhuang, J. (2014) analyzes the

governance gap and its effect on economic growth. Among many other results, the study

shows that “Middle East and North African countries with a surplus in political stability,

government effectiveness, and corruption control are observed to grow faster than those

with a deficit in these indicators by as much as 2.5 percentage points annually.” The

study implies that governance matters to economic growth in the MENA region.

Furthermore, Mehanna, Yazbeck, and Sarieddine (2010) study the relationship

between governance and economic development in 23 MENA countries over the period

1996-2005. Their study compares different challenges facing the region including

education, fixed investment, presence of religious fractionalization, and governance. The

study shows that improving governance is the main challenge facing the MENA

countries. The study shows that voice and accountability, government effectiveness, and

control of corruption exert the strongest economic impact on economic development.

Additionally, Emara, N. and Jhonsa (2014) shows that despite the low

performance of most of MENA countries on almost all the six measures of World Bank

Governance Indicators, their estimated levels per capita of income are relatively higher

than the rest of the countries in the sample. This study concludes that most of these

countries have achieved relatively high but fragile standard of living that is not based on

sound governance.

According to the latest available World Governance Indicator data for the voice

and accountability metric, shows that 16 of the Middle East and North Africa region’s 21

largest countries by population were given a negative governance score and ranked in the

38th percentile or lower. For the political stability metric, 15 out of 21 were given a

negative score and ranked in the 36th percentile or lower. For the government

effectiveness metric, 12 out of 21 nations had negative scores, and 3 out of 21 ranked

below the 25th percentile. For regulatory quality, 15 out of 21 had negative scores, and 6

out of 21 again ranked below the 25th percentile. For rule of law, 11 out of 21 had

negative scores, and 4 out of 21 ranked below the 25th percentile. And for control of

corruption, negative scores were given to 13 out of 21 nations, with 4 out of 21 ranking

below the 25th percentile.

Despite the MENA governments’ effort to enhance the level of governance, the

World Bank’s Governance Indicators show no significant change across all indicators,

namely rule of law, control of corruption, government effectiveness, voice and

accountability and regulatory quality for the MENA region over the period between 2007

and 2014. Of course looking at the MENA governance indicators, one can tell that the


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performance between these countries has been non-uniform. Countries such as Bahrain,

Cyprus, Israel, Oman, Turkey, and United Arab Emirates have performed relatively

better than the rest of the MENA countries. And with no doubt, given the recent political

instability in Syria, the data shows that Syria is the worst of the list of the MENA

countries in terms of all governance indicators. The data shows that Yemen and Iraq are

following Syria in terms of low levels of governance quality especially for the political

stability index.

The Open Budget Index of 2015 2 , which reflects governments’ social

accountability, shows that Saudi Arabia, Qatar, Lebanon, Iraq, Egypt, and Algeria have

recorded the lowest levels with a score of “scant or none (0-20)”. Furthermore, freedom

of the citizens to express their opinions in political matters and the freedom of the press

has been highly restricted in countries such as Egypt, Iran, and Saudi Arabia. As Pintak,

L. (2011) wrote about the Arab media’s poor standard of delivering services to its

citizens, “A free media is not necessarily a credible media.” So it’s not only a matter of

freedom, but it is also a matter of credibility.

In general, the extent to which citizens of the MENA region have confidence in

and abide by the rules of society have been generally very weak. The rule of law index is

relatively the worst for Iraq, Syria, and Yemen with an average of -1.29. Furthermore,

what makes matters worse for a countries with relatively strong legal framework such as

Egypt (score -0.60) is the problem of implementing such legislations. This means that the

problem of governance is not only about its existence but more importantly about the

mechanism through which it can be implemented to positively affect the society.

Additionally, countries such as Egypt, Algeria, Djibouti, Iraq, Syria, and Yemen

suffer from relatively high levels of corruption with an average index of -1.02. Some of

these countries have taken steps to fight corruption but still more efforts need to be done.

For instance, Egypt has signed many international projects to fight corruption such as the

MENA-OECD Task Force on Anti-Bribery, OECD Good Governance for Development

in Arab Countries Initiative, the Arab Anti-Corruption and Integrity Network (ACINET),

and the UNDP-POGAR project to support the Ministry of Investment in the fight against

corruption (OECD, 2009). However, no significant change has happened and a lot still

needs to be done from the side of the government such as developing a nationwide anti-

corruption strategy.

2 The Open Budget Initiative monitors the availability of seven key budget documents: Pre-Budget

Statement, Executive’s Budget Proposal, Enacted Budget, In-Year Reports, Mid-Year Reports, Mid-Year

Review, Year-End Report, and Audit Report. The index also records the presence of Citizens’ Budgets.


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Against the above background this study seeks to provide a comprehensive index

of governance and estimate its impact on economic growth. Specifically, this study will

attempt to answer the following questions: How does economic growth change as the

comprehensive index of governance changes? Which component of governance is more

important in explaining variations of economic growth among different countries? How

these results are interpreted for the MENA region?

This study is organized as follows: Section II presents the regression model and

the methodology of the principal component analysis. Section III discusses the data set

used. Section IV analyzes the estimation results. Section V concludes this study. Section

VI includes the references. Finally, the appendix appears after Section VI.

II. Empirical Specification

(i) Regression Model

Following Kaufmann and Kraay (2002), our regression model is presented below:

pgdpi = + *govi + ei (1)

Where pgdp is the log per capita income, gov is the governance index, e

represents all the other factors not included in this parsimonious equation, and finally the

subscripts i represents the country. The estimate of will provide information on the

marginal contribution of improving governance to the per capita gdp growth in the long

run.

We present the construction of composite governance index (CGI) using the

Principal Component Analysis (PCA) method in this section. Statisticians and data

scientists have long adopted this data reduction PCA method in their work. However, it’s

not popular in economists’ empirical tool bag yet.

(ii) Principal Components Analysis

Given a data matrix X with p variables and n observations, we can write it as the

following:

X = ; where i = 1…n, j = 1…p. (2)

Geometrically, the goal of the PCA is to project the data matrix X from p

dimensions to a smaller dimension k, where k << p, meanwhile keeping as much

information (i.e., variance maximization) as possible in this dimension-reduced data

pn1n

p212

p111

XX

XX

XX

,,

,,

,,

..

....

..

..


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matrix with the size n by k3. Specifically, the PCA method replaces a large number of

correlated variables (X1, … , Xp) with a smaller number of uncorrelated variables

(Principal Components; PC1, … , PCk).

Mathematically, the first principal component is a linear combination of X1 to Xp

observed variables that accounts for the largest variance among them:

PC1 = a1X1 + a2X2 + … + apXp (3)

In equation (3) the vector of coefficient aj (j = 1…p) is termed loading vector and

is normalized to avoid inflating the variance of PC1. By the same token, the second

principal component (PC2) is another linear combination of X variables that accounts for

the largest variance among them, however, with a constraint; PC2 is required to be

orthogonal to PC1. Theoretically, we are able to track as many principal components as

the number of variables (p of them) in the data matrix X. But in practice, we search for a

much smaller number of principal components (PCs) that is able to capture as much as

information from the original set of X variables. We present the algorithm for deriving

PCs in the following section.

(ii) Algorithm to derive PCs

The algorithm to uncover PCs is based on the singular value decomposition (SVD)

method (I.T. Jolliffe, 2002). While there is no specific rule to select the number of PCs,

we use four criteria to determine the appropriate number of PCs; they are Kaiser-Harris’s

stopping rule (criteria), Cattell’s Scree test, Parallel analysis and Percent of cumulative

variance (see J. Brown, an internet source on this topic).

First, capital letter W is used to denote the variance-covariance matrix. Where W

is related to data matrix X in the following form; W = 1n

XX T

, a p*p matrix and the

superscript “T” is the transpose operator. Since W is a symmetric matrix it can be

diagonalized as follows:

W = VVT (4)

In equation (4), V is a matrix of eigenvectors and is diagonal matrix with the

eigenvalues. The matrix V is essential in deriving PCs and it’s also termed Principal Axes.

Apply the SVD method to X and we can obtain the following:

3 Two excellent references that cover Principal Components Analysis method are “An Introduction to

Statistical Learning/ with Applications in R” & “R in Action”.


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X = UVT (5)

As mentioned earlier, X is the data matrix with dimension n by p. U and V are

both orthogonal squared matrix with dimension n and p, respectively. is diagonal with

diagonal entries that represent singular values.

There is a relationship between equation (4) and (5), that is:

W = 1n

XX T

=

1n

VUVU TTT

)()( =

1n

VV T2

(6)

Comparing equation (4) to (6), it can be seen that the square of singular values

(from ) is actually the eigenvalues derived from the diagonalization of W (or XTX).

Denote the eigenvalues j (j = 1…p). The size of each to the sum of all s

accounts for the proportion of variances in the original data matrix X that can be captured

by the corresponding principal component. If we rearrange in a descending order from

1 to p, 1 and the corresponding eigenvector (or first principal component PC1)

accounts for the largest proportion of variances in X. In practice, correlation of matrix X

is applied before deriving PCs to avoid scaling problem. To this end, the principal

components are derived by post-multiplying data matrix X with the principal axes V.

Alternatively, PCs can also be derived using the following equation:

XV = UVTV = U (7)

According to equation (7), principal components (PCs) can be obtained using

either one of the following outcome:

PCs XV U (8)

III. Data

The cross-sectional data set is obtained from the World Bank’s World Development

Indicators covering 188 countries for the years 2009 and 2013, with special focus on 21

MENA countries4. The reason to choose these two specific years for this study is to make

a comparison about the governance change before and after the Arab Spring that have

4 There are 22 MENA countries that include Algeria (DZA), Bahrain (BHR), Cyprus (CYP), Djibouti (DJI),

Egypt (EGY), Iran (IRN), Iraq (IRQ), Israel (ISR), Jordan (JOR), Kuwait (KWT), Lebanon (LBN), Libya

(LBY), Morocco (MAR), Oman (OMN), Qatar (QAT), Saudi Arabia (SAU), Syria (SYR), Tunisia (TUN),

Turkey (TUR), United Arab Emirates (ARE), West Bank and Gaza (WBG), and Yemen (YEM). Syria is

excluded in this study due to missing WGI data.

http://en.wikipedia.org/wiki/Algeria

http://en.wikipedia.org/wiki/Bahrain

http://en.wikipedia.org/wiki/Cyprus

http://en.wikipedia.org/wiki/Djibouti

http://en.wikipedia.org/wiki/Egypt

http://en.wikipedia.org/wiki/Iran

http://en.wikipedia.org/wiki/Iraq

http://en.wikipedia.org/wiki/Israel

http://en.wikipedia.org/wiki/Jordan

http://en.wikipedia.org/wiki/Kuwait

http://en.wikipedia.org/wiki/Lebanon

http://en.wikipedia.org/wiki/Libya

http://en.wikipedia.org/wiki/Morocco

http://en.wikipedia.org/wiki/Oman

http://en.wikipedia.org/wiki/Qatar

http://en.wikipedia.org/wiki/Saudi_Arabia

http://en.wikipedia.org/wiki/Syria

http://en.wikipedia.org/wiki/Tunisia

http://en.wikipedia.org/wiki/Turkey

http://en.wikipedia.org/wiki/United_Arab_Emirates

http://en.wikipedia.org/wiki/Yemen


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started in Tunisia in 2010. For governance indicators, the Worldwide Governance

Indicators is used which have been published annually since 1998. The data of the

Worldwide Governance Indicators is compiled at the World Bank by Kaufmann, Kraay,

and Zoido-Lobaton (1999) and Kaufmann, Kraay, and Mastruzzi (2005). These indicators

are based on some 30 opinion and perception-based surveys of various governance

measures from investment consulting firms, non-government organizations, think tanks,

governments, and multilateral agencies classified into six dimensions including

government effectiveness, political stability, control of corruption and regulatory quality,

voice and accountability, and rule of law5. Data on GDP per capita in 2005 purchasing

power parity terms is sourced from the World Development Indicators.

IV. Empirical Outcomes & Findings

We first report the loadings of the six principal components and the corresponding

eigenvalues in Table 1. How many principal components are needed to capture the most

variances in X? Kaiser–Harris criterion suggests retaining components with eigenvalues

that are greater than one. In the Cattell Scree test, the eigenvalues s are plotted against

their component numbers p. If a big bend is revealed, the components above this bend

will be kept. In Figure 1, the blue line flattens out after the second component which is

where the bend appears. In the Parallel analysis, a series of s are obtained based on

simulation. If the eigenvalues obtained from X are greater than the average of simulated

s, the corresponding principal components are selected. The cross symbols “x” in Figure

1 represent all the six eigenvalues. The three criteria presented in Figure 1 all indicate the

first principal component should be selected; the cross symbol at the top left corner.

While we do not show the percent of cumulative variance graphically, a quick

computation using the eigenvalues presented at the bottom of Table 1, we can find that

the first PC explains about 81%6 of variances from the original data set, X.

We transform the original WGIs to a single composite governance index using the

following computational process:

PC1 = X*L1 (9)

5 The detailed definition of each indicator is provided in the appendix.

6 = 0.8122 or 81.22% 045500493014140339405509087354

87354

......

.


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CGI = )max( 1

1

PC

PC*100 (10)

In equation (9), it shows that the first principal component is obtained by

multiplying both data matrix X and the first loading L1 (the first column in red ink in

Table 1). The Composite Governance Index (CGI) is the PC1 rescaled by dividing the

largest element in the PC1. The histogram of the CGI is reported in Figure 2. It’s a bit

skewed to the right. The median index is about -9.7. Our CGI indicates that Finland (FIN)

has the best governance index that equals 100 and Afghanistan (AFG) has the lowest

index that equals -88.85 among these 188 countries in our data. Based on the quartiles of

the CGI, we also report the ranking of governance of these 21 MENA countries in Table

2. Among these MENA countries, Cyprus has the best governance ranking and Iraq has

the lowest one.

Using equation (1), we run a regression of the log of per capita GDP on the CGI

and report the outcome in Table 3.1; the corresponding graphical outcome is presented in

Figure 3.1. In Table 3.1, due to small p-values, both the t-test and f-test support the

significance of slope estimate and validity of the model. The slope estimate indicates that

per capita GDP is going to grow by about 2% (0.0199) if the CGI increases by one unit.

The multiple or adjusted R2 says that 53% of variation in log of per capita GDP can be

explained by CGI.

We also conduct another regression that is only based on these 21 MENA

countries and report the outcome in Table 3.2 and Figure 3.2. It can be seen that the

estimated slope is 0.01804 that is a bit lower than the estimated slope of 0.0199 from the

whole sample of 188 countries. While the estimated slopes are similar, we do notice that

the adjusted R2 drops significantly to 35.9%.

To this end, we make a comparison of the CGI and log of per capita GDP in both

year 2009 and 2013 and summarize our findings in Table 4. The CGI in 2013 is obtained

using the same loading we derived in 2009. While we feel disappointed that the

improvement in CGI doesn’t fully coincide with the economic growth in the MENA

countries, however, the low adjusted R2 we found earlier may indicate that there are more

factors that are involved in these countries’ economic growth in addition to the soundness

of governance. For example, Fig. 3.1 shows that the MENA countries that are way above

the regression line are mostly oil rich countries.

The results of Table 4 reveal interesting points. Over the period 2009 to 2013,

only five of the countries in the MENA countries, namely United Arab Emirates, Algeria,

Iraq, Israel, and West Bank and Gaza, have experienced an improvement in CGI that was


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accompanied by an enhancement in economic growth. Additionally, over the same period

only one country in the sample, namely Iran, has experience an improvement in its CGI

with no change in economic growth. Furthermore, only four countries namely Cyprus,

Kuwait, Libya, Oman, and Yemen have experienced deterioration in their CGI that was

also accompanied with lower economic growth over the same period. Finally, or more

importantly, over the same period about fifty percent of the MENA countries, namely

Bahrain, Djibouti, Egypt, Jordan, Lebanon, Morocco, Qatar, Saudi Arabia, Tunisia, and

Turkey have experienced deterioration in the CGI that was accompanied by an increase

in economic growth.

V. Conclusion

There are two main contributions in this paper. The first contribution is that we were able

to create a “composite governance index” (CGI) that summarizes the existing six

governance measurements; the Worldwide Governance Indicators (WGI), using the

Principal Components Analysis (PCA). The first principal component derived from the

WGIs accounts for as much as 81% of the variations in the original six WGI

measurements, which indicates that it can be used as a strong indicator for evaluating

governments’ managerial ability and effectiveness. The second contribution is that we

were able to quantify the marginal contribution of improvement in governance on

economic performance using PPP adjusted constant per capita GDP data. We find that the

per capita GDP would rise by about 2% if the CGI increases by one unit. Using the Rule

of 70, the marginal estimate further indicates a mere five-unit improvement in CGI would

double a country’s per capita GDP in seven years.

Our results suggest that nine countries of the MENA region have shown a positive

correlation between governance and economic growth which includes those countries that

have experience deterioration accompanied by deterioration and those countries that have

experienced an enhancement accompanied by an enhancement in governance index and

in economic growth, respectively. The relatively low R2 of 35.9% confirms these results.

More specifically, the CGI explains only 35.9% of the variations in economic growth in

the MENA region. Our results go in line with the findings of Emara and Jhonsa (2014)

that the majority of the MENA countries have achieved fragile levels of economic growth

that does not depend on sound governance. Our next step in this research is to include

more control variables in the MENA regression model and we hope that, by doing this,

we can have a better qualitative prediction outcome on the link between governance and

growth in this region.


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wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2003/11/06/000090341_20031

106135835/Rendered/PDF/271460PAPER0Be1ance0for0development.pdf.

Zhuang, J., E. de Dios, and A. Lagman-Martin (2010), “Governance and Institutional

Quality and the Links with Growth and Inequality: How Asia Fares”. In J. Zhuang, ed.

Poverty, Inequality, and Inclusive Growth in Asia: Measurement, Policy Issues, and

Country Studies. London: Anthem Press and Manila: Asian Development Bank.

UNDP (2010), “Voice and Accountability for Improved Service Delivery”, UNDP

Regional Training Event for the Arab States, October, Cairo, Egypt.

http://www.undp.org/content/dam/undp/documents/partners/civil_society/facilitating_citi

zen_action/VA_background_paper_final.pdf.

Appendix

Table A Governance Indicators and Definitions

1- Voice and

accountability

Measured by the extent to which a country’s citizens are able to

participate in selecting their government as well as freedom of

expression, association, and the press.

2- Political stability and

absence of violence

Measured by the likelihood that a government will be destabilized by

unconstitutional or violent means, including terrorism.

3- Government

effectiveness

Measured by the quality of public services, the capacity of civil

services and their independence from political pressure, and the

quality of policy formulation.

4- Regulatory quality Measured by the ability of a government to provide sound policies

and regulations that enable and promote private sector development.

5- Rule of law Measured by the extent to which agents have confidence in and abide

by the rules of society, including the quality of property rights, the

police and the courts, and the risk of crime.

6- Control of corruption Measured by the extent to which public power is exercised for private

gain, including both petty and grand forms of corruption as well as

elite “capture” of the state.

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140

Figure 1 Principal Component Selection Criteria

Figure 2 The Distribution of Composite Governance Index

1 2 3 4 5 6

01

23

45

Three Criteria to Select PCA

Component Number

eige

n va

lues

of p

rincip

al c

ompo

nent

s

Kaiser-Harris criterion

Cattell Scree test

Parallel analysis

Composite Governance Index

CDI

Freq

uenc

y

-50 0 50 100

05

1015

2025


141

Figure 3.1 Linear Regression for All 188 Countries

Figure 3.2 Linear Regression for 21 MENA Countries

-50 0 50 100

78

910

11

Economic Growth vs CGI

Composite Governance Index (CGI)

log

(per

cap

ita G

DP)

ARE

BHR

CYP

DJI

DZA

EGY

IRN

IRQ

ISR

JOR

KWT

LBN

LBY

MAR

OMN

QAT

SAU

TUN

TUR

WBGYEM

log (per capita GDP) = 9.0722 + 0.0199*CGI

-60 -40 -20 0 20 40 60

89

1011

Economic Growth vs CG

(MENA Country)

Composite Governance Index (CGI)

log

(per

cap

ita G

DP)

ARE

BHR

CYP

DJI

DZA

EGY

IRN

IRQ

ISR

JOR

KWT

LBN

LBY

MAR

OMN

QAT

SAU

TUN

TUR

WBGYEM

log (per capita GDP) = 9.9860 + 0.0180*CGI

Regression Line (188 Countries)


142

Table 1 Loadings of Principal Components

WGI\Loadings of PCs L1 L2 L3 L4 L5 L6

Control of Corruption (corr) 0.4303 -0.062 0.2557 -0.6295 0.5808 -0.1095

Government Effectiveness (ef) 0.431 -0.2729 0.2576 0.0263 -0.2887 0.7677

Political Stability (ps) 0.3377 0.8773 0.1642 0.2728 0.0836 0.0892

Regulatory Quality (rq) 0.415 -0.3868 0.0367 0.6964 0.337 -0.2799

Rule of Law (rl) 0.4424 -0.02 0.1273 -0.1736 -0.6771 -0.5469

Voice & Accountability (va) 0.3835 0.0454 -0.9076 -0.1167 0.0155 0.1152

Eigenvalues () 4.8735 0.5509 0.3394 0.1414 0.0493 0.0455

Table 2 Ranking According to the Quartiles

Country CGI Rank Country CGI Rank

ARE 28.10 2 KWT 13.56 2

BHR 12.67 2 LBN -30.70 3

CYP 58.25 1 LBY -45.35 4

DJI -26.30 3 MAR -13.30 3

DZA -41.87 4 OMN 16.69 2

EGY -19.85 3 QAT 43.52 1

IRN -58.69 4 SAU -13.49 3

IRQ -71.26 4 TUN -3.55 2

ISR 30.73 2 TUR 0.94 2

JOR 3.08 2 WBG -34.46 4

YEM -59.40 4

Notes: Rank = 1 if CGI > 35.4, Rank = 2 if -9.7 < CGI 35.4, Rank = 3 if -34.1 < CGI -9.7,

Rank = 4 if CGI -34.1


143

Table 3.1 Impact of GCI on Economic Growth- All Countries

Dependent variable: Log Per Capita GDP

Estimation Method: Linear Regression Model

CGI 0.020***

(0.001)

Intercept 9.072***

(0.062)

Countries/Observations 188

F (1, 186) statistic = 210 p-value: < 2e-16

R-Squared 0.53

Notes: ***, ** and * denotes statistical significance at the 1%, 5% and 10% levels

respectively. Numbers in round parentheses (.) are the robust standard errors.

Table 3.2 Impact of GCI on Economic Growth- MENA Countries

Dependent variable: Log Per Capita GDP

Estimation Method: Linear Regression Model

CGI 0.018***

(0.001)

Intercept 9.986***

(0.186)

Countries/Observations 21

F (1, 19) statistic = 12.2 p-value: 0.0024

R-Squared 0.36

Notes: ***, ** and * denotes statistical significance at the 1%, 5% and 10% levels

respectively. Numbers in round parentheses (.) are the robust standard errors.


144

Table 4: The Change in CGI and Growth Between the Years 2009 and 2013

Country CGI/09 CGI/13 Improve logY/09 logY/13 Growth

ARE 28.10 32.26 4.16 11.02 11.02 YES

BHR 12.67 -2.24 -14.91 10.61 10.64 YES

CYP 58.25 51.03 -7.22 10.44 10.31 NO

DJI -26.30 -36.58 -10.27 7.86 7.98 YES

DZA -41.87 -40.31 1.56 9.44 9.49 YES

EGY -19.85 -43.65 -23.80 9.19 9.22 YES

IRN -58.69 -55.06 3.63 9.69 9.66 NO

IRQ -71.26 -67.17 4.09 9.40 9.63 YES

ISR 30.73 33.98 3.26 10.26 10.34 YES

JOR 3.09 -6.42 -9.50 9.33 9.34 YES

KWT 13.56 -3.35 -16.91 11.29 11.22 NO

LBN -30.70 -34.57 -3.87 9.66 9.71 YES

LBY -45.35 -75.04 -29.69 10.24 9.88 NO

MAR -13.30 -16.00 -2.69 8.74 8.84 YES

OMN 16.69 6.84 -9.84 10.77 10.57 NO

QAT 43.52 37.57 -5.95 11.70 11.82 YES

SAU -13.49 -13.64 -0.14 10.68 10.80 YES

TUN -3.55 -14.09 -10.54 9.23 9.28 YES

TUR 0.94 -1.84 -2.78 9.65 9.83 YES

WBG -34.46 -33.21 1.25 8.34 8.41 YES

YEM -59.40 -64.84 -5.45 8.38 8.21 NO

Notes: CGI: composite governance index, Improve = CGI/13 – CGI/09

logY = natural log of per capita GDP, Growth: whether logY/13 > logY/09

the impact of governance on economic growth: the case of middle

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